Search

In recent years, additive manufacturing has been widely used in industrial, medical, and educational fields. Material extrusion is used in most industries to increase development efficiency and reduce costs. This study used the material extrusion to discuss the print quality of additive manufacturing and optimized the processing parameters based on material properties. Based on the literature, this study summarized the fishbone diagram influencing printing quality. The layer height, nozzle temperature, printing speed, infill pattern, and filling spacing were selected as the control factors of the Taguchi method. An orthogonal array L16 was used for parameter design. The optimal parameters were analyzed using the variance and the response surface method. The results of the study are as follows.

This study proposes a comprehensive methodology for accurate State of Charge (SOC) estimation in lithium-ion batteries by optimizing equivalent circuit model (ECM) parameters under varying temperature conditions using the Taguchi method. Analysis of Variance (ANOVA) was employed to evaluate the influence of these parameters on ECM accuracy. Experiments were conducted at −10 °C, 25 °C, and 40 °C to evaluate the effects of pulse time gap, discharge pulse time, and C-rate on SOC estimation accuracy. A genetic algorithm-based multi-objective optimization technique was employed to minimize RMSE in the extended Kalman filter (EKF) SOC estimation process. The results showed that temperature significantly impacts SOC prediction, with deviations most pronounced at low (−10 °C) and high (40 °C) temperatures. When assessments are conducted for different SOC levels (SOC90, SOC50, SOC30), the key results highlight the substantial influence of pulse time gap and discharge pulse time on model accuracy. Also, it was observed that there is a significant reduction in RMSE, indicating improved performance under optimized conditions. The findings are particularly relevant for real-time applications, such as electric vehicles, where accurate SOC estimation is crucial for battery management.

This study looks at how welding intensity, speed, voltage, and stick-out affect the structural and mechanical characteristics of metal inert gas (MIG) welding on SUS 201 stainless steel and C20 steel. The Taguchi method is used to optimize the study’s experiment findings. The results show that the welding current has a more significant effect on the tensile test than the welding voltage, stick-out, and welding speed. Welding voltage has the lowest influence. In addition to the base metals’ ferrite, pearlite, and austenite phases, the weld bead area contains martensite and bainite microstructures. The optimal parameters for the ultimate tensile strength (UTS), yield strength, and elongation values are a 110 amp welding current, 15 V of voltage, a 500 mm.min−1 welding speed, and a 10 mm stick-out. The confirmed UTS, yield strength, and elongation values are 452.78 MPa, 374.65 MPa, and 38.55%, respectively, comparable with the expected value derived using the Taguchi method. In the flexural test, the welding current is the most critical element affecting flexural strength. A welding current of 110 amp, an arc voltage of 15 V, a welding speed of 500 mm.min−1, and a stick-out of 12 mm are the ideal values for flexural strength. The flexural strength, confirmed at 1756.78 MPa, is more than that of the other samples. The study’s conclusions can offer more details regarding the dissimilar welding industry.

This study examines the dry milling process of a rare-earth-based magnesium alloy, emphasizing the optimization of the milling parameters and their impact on the surface quality, cutting forces, and the rate of material removal. The objective is to improve our comprehension of the milling behavior of the Mg-Gd-Y-Er alloy. The Taguchi technique is adopted to formulate the experimental design. This study methodically investigates the influence of heat treatment (T4 and T6) on milling performance, and the effects of speed, feed rate, and depth of cut. The output variables considered for this investigation are the surface roughness (Ra, Rz, Sa, and Sz), material removal rate (MRR), and cutting force. To optimize the milling parameters and achieve superior outcomes, the multi-objective optimization technique TOPSIS is used. At a feed rate of 150 mm/min, a spindle speed of 1500 rpm, and a depth of cut of 1 mm, the T4-treated sample exhibits a minimum surface roughness value of 0.0305 µm. The highest resultant force values of 96.4416 N and 176.1070 N for 200 °C and 225 °C T6-treated alloys are obtained by combining process parameters such as a spindle speed of 1500 rpm, a feed rate of 50 mm/min, and a depth of cut of 1.5 mm. Furthermore, the maximum closeness coefficient value is achieved by combining a spindle speed of 1000 to 1500 rpm, a feed rate of 150 mm/min, and a depth of cut of 0.5 mm to 1 mm. The closeness coefficient value is significantly influenced by the most significant process parameters, as indicated by the ANOVA results.

This study examines the effects of stick-out, welding current, welding speed, and voltage on the mechanical characteristics and microstructure of MIG welding on SUS 304 stainless steel and S20C steel. The Taguchi method was used to maximize the experiment’s outcomes. Fine columnar dendrites formed at fusion sites, and δ-ferrite phases with dark lines and shapes accumulated between the fusion line and the austenite phases. A welding current of 110 A, voltage of 15 V, welding speed of 500 mm/min, and stick-out of 12 mm were the optimal settings for the ultimate tensile strength (UTS). The UTS value confirmation was 469.4 MPa, which agrees with the estimated value determined using the Taguchi technique. The tensile test revealed that welding current had a far greater impact on mechanical qualities than welding voltage, speed, and stick-out distance. The ideal welding parameters for flexural strength were as follows: stick-out of 12 mm, arc voltage of 15 V, welding speed of 450 mm/min, and welding current of 110 amp. The Taguchi method is useful, as evidenced by the validation of the flexure strength of 1937.45 MPa, which is much greater than the other samples. The impact of the thermal annealing process on the mechanical characteristics of the dissimilar weld joints could be the subject of future research. The investigation results may offer more insightful information about the dissimilar welding field.

Dynamic random access memory (DRAM) serves as a critical component in medical equipment. Given the exacting standards demanded by medical equipment products, manufacturers face pressure to improve their product quality. The electrical characteristics of these products are based on the resistance value of the DRAM components. Hence, the purpose of this study is to optimize the resistance value of DRAM components in medical equipment. We proposed a novel FMEA-TSTM-NNGA framework that integrates failure mode and effect analysis (FMEA), the two-stage Taguchi method (TSTM), neural networks (NN), and genetic algorithms (GA) to optimize the manufacturing process. Moreover, the proposed FMEA-TSTM-NNGA framework achieved a substantial reduction in experimental trials, cutting the required number by a factor of 85.3 when compared to the grid search method. Our framework successfully identified optimal manufacturing condition settings for the resistance values of DRAM components: Depo time = 27 s, Depo O2 flow = 151 sccm, ARC-LTO etch time = 43 s, ARC-LTO etch pressure = 97 mTorr, Ox-SiCO etch time = 91 s, Ox-SiCO gas ratio = 22%, and Polish time = 84 s. The results helped the case company improve the resistance value of DRAM components from 191.1 × 10−3 Ohm to 176.84 × 10−3 Ohm, which is closer to the target value of 176.5 × 10−3 Ohm. The proposed FMEA-TSTM-NNGA framework is designed to operate efficiently on resource-constrained, facilitating real-time adjustments to production attributes. This capability enables DRAM manufacturers to swiftly optimize product quality.

This study addresses the issue of poor air quality and thermal comfort in rural outdoor toilets by proposing a ventilation system powered by a building-applied photovoltaic (BAPV) roof. A numerical model is established and validated through comparison with the literature and experimental data. Based on a consensus, four influential variables, namely, inlet position, outlet height, supply air temperature, and ventilation rate, are selected for optimization to achieve multiple objectives: reduction in ammonia concentration, a predicted mean vote (PMV) value of 0, minimization of age of air, and energy consumption. The present study represents a pioneering effort in integrating the Taguchi method, computational fluid dynamics (CFD), and grey relational analysis to concurrently optimize the influential variables for outdoor toilet ventilation systems through design and simulation. The results indicate that all four variables exhibit nearly equal importance. Ventilation rate demonstrates a dominant effect on ammonia concentration and significantly impacts the age of air and energy consumption, while supply air temperature noticeably influences PMV. The optimal scheme features an inlet at center top position, an outlet height of 0.2 m, a supply air temperature of 12 °C and a ventilation rate of 20 times/h. This scheme improves ammonia concentration by 18.9%, PMV by 6.8%, and age of air by 30.0% at a height of 0.5 m, while achieving respective improvements by 18.9%, 5.5%, and 22.2% at a height of 1.5 m. The BAPV roof system generates an annual electricity output of 582.02 kWh, which covers the energy consumption of 358.1 kWh for toilet ventilation, achieving self-sufficiency. This study aims to develop a zero-carbon solution for outdoor toilets that provides a safe, comfortable, and sanitary environment.

This paper deals with the topic of reducing drag force acting on aircraft wings by incorporating novel winglet designs, such as multi-tip, bird-type, and twisted. The high-speed NASA common research model (CRM) was selected as the baseline model, and winglet designs were retrofitted while keeping the projected wingspan constant. Computational analysis was performed using RANS coupled with the Spalart–Allmaras turbulence model to determine aerodynamic coefficients, such as CL and CD. It was observed that the multi-tip and bird-type designs performed exceptionally well at a low angle of attack (0°). A parametric study was conducted on multi-tip winglets by tweaking the parameters such as sweep angle (Λ), tip twist (Є), taper ratio (λ), and cant angle (Φ). The best combination of parameters for optimal aerodynamic performance while maintaining the wing root bending moment was determined using both the Taguchi method and Taguchi-based grey relational analysis (T-GRA) coupled with principal component analysis (PCA). Also, the percentage contribution of each parameter was determined by using the analysis of variance (ANOVA) method. At the design point, the optimized winglet design outperformed the baseline design by 18.29% in the Taguchi method and by 20.77% in the T-GRA coupled with the PCA method based on aerodynamic efficiency and wing root bending moment.

The powder metallurgy process of manufacturing the motor core and inductor core using SMC greatly changes formability depending on the process variables. Therefore, this study explored the optimal process conditions of the powder metallurgy of the SMC stator core using Fe-6.5 wt.%Si by applying the Taguchi method, and selected deviations between the maximum and minimum relative densities as characteristic values; selected the formation pressure, molding temperature, and heating time as control factors; and derived the process conditions with the maximum SNR. As a result, the molding pressure was 120 MPa, the molding temperature was 500 °C, and the heating time was 120 s, and the material properties of the electrical properties’ core loss, saturation flux density, and bulk conductivity were measured and analyzed. After that, a prototype was produced, the analysis was verified, the mechanical properties were verified by performing density and SEM analysis at 15, 9, and 3 mm points based on the press vertical direction, and a motor was manufactured to verify the electrical properties.

The main aim of this research was to investigate the aluminum AlSi9Cu3(Fe) machining chips recycling possibility utilizing a direct hot extrusion process and thixoforming. The thixo feedstock was prepared directly from the aluminum alloy AlSi9Cu3(Fe) machining chips waste without any remelting step. The machining chips were compacted, and direct hot extruded to create the solid samples and thixo feedstock. The aluminum alloy AlSi9Cu3(Fe) machining chips had a high degree of plastic deformation and after extrusion and heating in the semisolid temperature range, the suitable globular microstructure was achieved, which is a precondition for a successful thixoforming process. This approach can be characterized as a semisolid recycling process with a lower energy consumption, a higher material yield, and reduced greenhouse gas emissions into the atmosphere compared with conventional casting and recycling. Optical metallography, scanning electron microscopy accompanied with energy dispersive spectroscopy, electrical conductivity, and mechanical properties investigation were performed on the reference casted sample with a dendritic microstructure, the extruded sample with a severely deformed microstructure, and finally the thixoformed samples with a globular microstructure produced with different parameters, according to the Taguchi L4 (23) experimental plan.

This study systematically investigates the impact of the material properties of soft magnetic composites (SMCs) on the powder metallurgy forming process. It proposes a suitable material selection process for various motor types and shapes and determines the optimal forming conditions for each SMC material. This study employed the Taguchi design method to identify key control factors such as powder type, forming temperature, and forming speed, and analyzed their effects on relative density. Simulation results indicated that AncorLam HR exhibited superior properties compared with AncorLam and Fe-6.5wt.%Si. The optimal conditions determined through signal-to-noise ratio (SNR) calculations were AncorLam HR at 60 °C and five cycles per minute (CPMs). Validation through simulation and SEM analysis confirmed improved density uniformity and reduced defects in products formed under optimal conditions. Final prototype testing demonstrated that the selected conditions achieved the target density with minimal variance, enhancing the mechanical properties and performance of the motors. These results suggest that the appropriate application of SMC materials can significantly enhance motor efficiency and reliability.

A validation of the factorial, Taguchi and response surface methodology (RSM) statistical models is developed for the analysis of mechanical tests of hybrid materials, with an epoxy matrix reinforced with natural Chambira fiber and synthetic fibers of glass, carbon and Kevlar. These materials present variability in their properties, so for the validation of the models a research methodology with a quantitative approach based on the statistical process of the design of experiments (DOE) was adopted; for which the sampling is in relation to the design matrix using 90 treatments with three replicates for each of the study variables. The analysis of the models reveals that the greatest pressure is obtained by considering only the source elements that are significant; this is reflected in the increase in the coefficient of determination and in the predictive capacity. The modified factorial model is best suited for the research, since it has an R2 higher than 90% in almost all the evaluated mechanical properties of the material; with respect to the combined optimization of the variables, the model showed an overall contribution of 99.73% and global desirability of 0.7537. These results highlight the effectiveness of the modified factorial model in the analysis of hybrid materials.

Regular inspections and hull cleanings are essential to prevent bio-fouling on ships. However, traditional cleaning methods such as brush cleaning and high-pressure water-jet cleaning at docks are ineffective in cleaning niche areas like bow thrusters and sea chests. Consequently, cleaning robots based on brushes and water jets have been developed to effectively remove bio-fouling. However, there are concerns that brushes may damage hull coatings, allowing bio-fouling to penetrate the damaged areas. In this study, removal experiments were conducted to identify the most dominant factor in fouling removal using water jet-based cleaning, in preparation for the development of non-contact cavitation high-pressure water jet-cleaning robots. The Taguchi method was used to identify influential factors and generate experimental conditions, and equipment systems for the removal experiments were established. Image analysis was performed to assess the bio-fouling occurrences on each specimen before and after cleaning, and numerical simulations of the nozzle were conducted to estimate stagnation pressure and wall shear stress to confirm the effect on micro-fouling removal. The results indicated that pump pressure is the most influential factor in removing large bio-fouling organisms grown in marine environments and on ship surfaces.

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise.

Developing environmentally friendly building materials with low environmental impacts is receiving more attention nowadays to face the global challenges of climate change; building insulation materials made from agricultural waste can be used for their low environmental impact and to generate responsible supplies that utilize natural resources adequately. The study aims to assess a panel made from rice husk using the pulping method. An experimental design established the proportion of rice husk, the percentage of additive (NaOH concentration), boiling time and blending time. Taguchi’s method was applied to investigate the effect on density and thermal conductivity; the final panel with optimum conditions was morphologically analyzed using scanning electron microscopy (SEM); the thermal behavior was studied by thermogravimetric analysis (TGA); fire reaction and smoldering behavior were analyzed; and characterization in water absorption and acoustic absorption performances were established. The results show thermal conductivity values between 0.037 and 0.042 W/mK, a smoldering velocity of 3.40 mm/min, and a good acoustic absorption coefficient in octave frequency bands between 125 Hz and 4 kHz greater than 0.7. These characteristics are competitive with other insulating bio-based materials available on the market. This study employed chemicals utilized by other biomaterials for the pulping process and in flame retardants. However, it is important to investigate natural treatments or those with a diminished environmental impact.

This study involved the optimization of the molded pieces manufacturing process from a poly-3-hydroxybutyrate-co-3-hydroxyvalerate biocomposite containing 30% wood flour by mass. The amount of wood flour and preliminary processing parameters were determined on the basis of preliminary tests. The aim of the optimization was to find the configuration of important parameters of the injection process to obtain molded pieces of good quality, in terms of aesthetics, dimensions, and mechanical properties. The products tested for quality were dog bone specimens. The biocomposite was produced using a single-screw extruder, whereas molded pieces were made using an injection molding process. The Taguchi method was applied to optimize the injection molding parameters, which determine the products quality. Control factors were selected at three levels. The L27 orthogonal plan was used. For each set of input parameters from this plan, four processing tests were performed. The sample weight, shrinkage, elongation at break, tensile strength, and Young’s modulus were selected to assess the quality of the molded parts. As a result of the research, the processing parameters of the tested biocomposite were determined, enabling the production of good-quality molded pieces. No common parameter configuration was found for different optimization criteria. Further research should focus on finding a different range of technological parameters. At the same time, it was found that the range of processing parameters of the produced biocomposite, especially processing temperature, made it possible to use it in the Wood Polymer Composites segment.

To reduce the iron loss and torque ripple of an external rotor consequent pole (ERCP) motor used in an electric vehicle air-conditioning compressor, the magnetic pole structure of the motor was improved, and an unequal piecewise consequent pole (CP) structure was designed. The performance of the motor is optimized by reducing the harmonic content in the air gap flux density and reducing the iron saturation degree of the motor. The designed CP structure can significantly reduce the iron loss and torque ripple of the motor. Based on the Taguchi method, the optimal size parameters of the unequal piecewise CP structure are determined, and the final optimization design scheme is obtained. The results of finite element simulation and high-precision iron loss model show the following: compared with the original motor, the iron loss and torque ripple of the motor with the final optimized design scheme are significantly reduced.

Biomass combustors provide space heating by converting chemical energy in woody biomass into low-temperature thermal energy. Thermoelectric generators (TEGs) can generate electricity from the heat flux without significantly reducing heating performance. However, most current TEGs are small (40 mm × 40 mm), requiring many TEG elements to generate useful power from a biomass combustion-based space heater. This work compares the electrical generation potential of an array of small TEGs with a larger (80 mm × 120 mm) TEG in a vertical configuration representative of a residential heating appliance. An experimental facility was developed for various representative cold-side ducts and controllable hot-side temperature and cooling airflows, and the Taguchi method was used to evaluate the impacts of temperature, airspeed, and ducting configurations. The results indicate that temperature and airspeed significantly influence TEG power, while ducting configurations have an insignificant influence. The large TEG achieved more consistent temperatures but produced lower power than an array of smaller TEGs with the same total area. The study emphasizes optimizing TEG design and operating conditions to enhance electricity generation efficiency in space heating combustors.

In this study, a multi-objective optimization regarding the tribological characteristics of the hybrid composite with a base material of aluminum alloy A356 as a constituent, reinforced with a 10 wt.% of silicon carbide (SiC), size 39 µm, and 1, 3, and 5 wt.% graphite (Gr), size 35 µm, was performed using the Taguchi method, gray relational analysis (GRA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making methods. Tribological tests were carried out on a “block on disc” type tribometer with lubrication. Load, sliding speed, and graphite mass concentration were analyzed as input parameters. As output parameters, wear rate and coefficient of friction were calculated. An analysis of variance (ANOVA) was conducted to identify all parameters that have a significant influence on the output multi-response. It was found that the normal load has the highest influence of 41.86%, followed by sliding speed at 32.48% and graphite addition at 18.47%, on the tribological characteristics of composites. Multi-objective optimization determined that the minimal wear rate and coefficient of friction are obtained when the load is 40 N, the sliding speed is 1 m/s, and the composite contains 3 wt.% Gr. The optimal combination of parameters achieved by GRA was also confirmed by the TOPSIS method, which indicates that both methods can be used with high reliability to optimize the tribological characteristics. The analysis of worn surfaces using scanning electron microscopy revealed adhesive and delamination wear as dominant mechanisms.

Brushless Direct Current (BLDC) motors have seen significant improvements across various electrical applications. The growing focus on motor design research highlights the BLDC motor’s superior efficiency compared to traditional motors, which consume more power. BLDC motors are compact, lightweight, energy-efficient, and easy to control, making them ideal for modern applications. This study aims to enhance BLDC motor design and performance by employing the Taguchi method, Response Surface Methodology (RSM), and Finite Element Method (FEM) for multi-stage optimization. A 26-watt BLDC electric fan motor is the reference model for this study. The Taguchi method helps identify optimization points, guiding further enhancements in the second stage. The study proposes a design with improved output power, torque, and efficiency. The final design achieves a 15% higher energy efficiency than the reference model, with a 10 W increase in output power and a 0.032 Nm increase in maximum torque. The FEM analysis using JMAG software v 21.2 validates the proposed design, which shows improved configurations compared to the reference model, demonstrating the efficiency of the optimization techniques for BLDC motor design.

This research investigates the control of thickness and weight in plastic extrusion vacuum-thermoforming products to identify optimal key parameters for cost reduction and energy savings. The initial step involves identifying crucial influencing factors. In this step, the Delphi technique was employed through a questionnaire administered to a panel of expert scholars to ensure minimal error and maximal reliability in determining key influencing factors. Consensus was sought to establish appropriateness and consistency. Subsequently, the Taguchi method was applied for quality design and planning of the extrusion vacuum-forming process. The experimental design parameters were selected using an L18 (21 × 37) orthogonal array, and the desired quality characteristics were determined. Comparative analysis of quantitative production data from two consecutive experiments was conducted, and based on F-values and contribution analysis, the combination of control factors maximizing the Signal-to-Noise (S/N) ratio was identified. The objective is to seek optimal parameters for improving the quality of the plastic polypropylene (PP cup lid) manufacturing process, reducing process variability, and identifying the most robust production conditions. Through multiple actual production prediction experiments, it was determined that five control factors, “polypropylene new material ratio,” “T-die lips adjustment thickness”, “mirror wheel temperature stability”, “molding vacuum pressure time”, and “forming mold area design”, contribute to the maximization of the S/N ratio, i.e., minimizing variability. Statistical validation confirms a significant improvement in product quality and weight control. Noteworthily, the quality control model and experimental design parameters established in this study are also applicable to other plastic products and bio-based materials, such as PET, HIPS, and biodegradable PLA lids with added calcium carbonate. The results of the experimental production demonstrate its ability to consistently control product weight within the range of 3.4 ± 0.1 g, approaching the specified tolerance limits. This capability results in approximately 2.6% cost savings in product weight, contributing significantly to achieving a company’s KPI goals for environmental conservation, energy efficiency, and operational cost reduction. Therefore, the findings of this study represent a substantial and tangible contribution.

In this study, the optimization of the operational parameters for a single proton exchange membrane fuel cell (PEMFC) was carried out using the Taguchi method and orthogonal array. The operating parameters were H2 stoichiometry, air stoichiometry, cell temperature, and back pressure of the anode∙cathode, each with three levels. The performance of the PEMFC, operated according to the L9 orthogonal arrangement, was evaluated through I–V curves at a step-up current loading ranging from 0.1 to 0.7 A/cm2. The results indicated that the anode∙cathode back pressure had the greatest sensitivity to the output voltage compared to the other operating parameters. Increasing the back pressure resulted in higher current output densities at higher values than those applied in the orthogonal arrangement. As the back pressure increased, the output voltage tended to increase at each current density. However, for operating conditions above 150 kPa, the improvement in cell performance was either not significant or tended to decrease. Therefore, it can be concluded that the Taguchi method and orthogonal array are effective tools for selecting the optimal operating conditions for PEMFC.

This article presents a novel bonding method for chip packaging applications in the semiconductor industry, with a focus on downsizing high-density and 3D-stacked interconnections to improve efficiency and performance. Microfluidic electroless interconnections have been identified as a potential solution for bonding pillar joints at low temperatures and pressures. However, the complex and time-consuming nature of their production process hinders their suitability for mass production. To overcome these challenges, we propose a tailored plating solution using an enhanced copper concentration and plating rate. By eliminating the need for fluid motion and reducing the process time, this method can be used for mass production. The Taguchi approach is first used to optimize the copper–quadrol complex solution with the plating rate and decomposition time. This solution exhibits a copper concentration that is over five times higher than that of conventional solutions, a plating rate of 22.2 μm/h, and a decomposition time of 8 min on a Cu layer substrate. This technique enables Cu pillars to be successfully bonded within 7 min at 35 °C. Planarizing the pillar surface yields a high bonding percentage of 99%. Mechanical shear testing shows a significant fracture strength of 76 MPa.

A new type of interior combined pole permanent magnet drive motor is proposed in this paper, which aims to improve the power and comfort of electric vehicles. In view of the complex magnetic circuit structure and rich harmonic magnetic field of the motor, the initial magnetic pole parameters of the rotor are determined by the equivalent magnetic circuit method. Then, aiming at the complex magnetic circuit and rich harmonic magnetic field, a multi-objective optimization method based on the Taguchi method and response surface method is proposed to reduce the cogging torque, high harmonic content in air gap magnetic flux density and increase the output torque. Based on the finite element analysis of the electromagnetic performance of the new type interior combined magnetic pole permanent magnet drive motor before and after optimization, it can be seen that the improved rotor structure can effectively reduce the torque ripple and increase the torque density. Finally, a prototype was developed and experiments were conducted, and experimental results verified the correctness of the proposed multi-objective optimization algorithm.

This paper aims to optimize a pulsed electrophoretic deposition (EPD) process for TiO2 films. This is accomplished by determining the optimal configuration of the coating parameters from a robust optimization perspective. The experimental study uses a composite central design (CCD) with four control factors, i.e., the initial concentration (x1 in g/L), the deposition time (x2 in s), the duty cycle (x3 in %), and the voltage (x4 in V). The process responses that should all be maximized are the photocatalytic efficiency of the thin film (De) and three critical charges, which characterize the adhesion failure, i.e., LC1: the load at which the first cracks occurred; LC2: the load at which the film starts to delaminate at the edge level of the scratch track; and LC3: the load when the damage of the film exceeds 50%. This paper compares the robust optimization design of the EPD process using two methods: the robust design of processes and products using the stochastic frontier (RDPP-SF) and the surface response and desirability function methods. The findings show that the RDPP-SF method is superior to the response surface–desirability method for the process responses De and LC2 because of non-natural sources of variation; however, both methods perform comparably well while analyzing the LC1 and LC3 responses, which are subjected to pure random variability. The parameters setting for the process robust optimization are met in run 25 (x1 = 14 g/L, x2 = 150 s, x3 = 50%, and x4 40 V).

This study’s novel 13Ni400 maraging steel parts are additively manufactured through a selective laser melting process. The Taguchi approach is adopted to evaluate the combined influence of process variables (energy density), viz., laser power, layer thickness, hatch spacing, and scan speed, on responses like relative density, microhardness, surface roughness, and tensile strength. The powder and material characterization studies are conducted in terms of an optical microscope, scanning electron microscope (SEM), electron backscatter diffraction (EBSD), X-ray diffraction (XRD), and fractography analysis to explore the pre- and post-fabrication scenarios of the build parts. The consequences of energy density and process variables are studied through meticulous parametric studies. Finally, the optimum level of built parameters is identified and validated by a confirmative test predicting an average error of ~1.80%. This work is proficient in producing defect-free parts with maximum densification and improved mechanical properties for newly developed 13Ni-400 maraging steel by the selective laser melting (SLM) technique.

For electrical machines with complex structures, the design space of parameters can be large with high dimensions during optimization. Considering the calculation cost and time consumption, it is hard to optimize all the design parameters at the same time. Therefore, in that situation, sensitivity analysis of these design parameters is usually used to sort out crucial parameters. In this paper, the sensitivity analysis-based Taguchi method is applied to optimize the axial-flux permanent magnet (AFPM) machine with yokeless and segmented armature (YASA) topology for an in-wheel traction system. According to the key parameters and their sensitivity analysis, the optimal machine scheme to meet the performance requirements can be formed. In this case study, the machine performance is improved significantly after optimization. Lastly, the experimental results verify the accuracy of the model used in this study.

Patients with lower limb amputation usually use prosthetic feet. Elastomeric material is an important part of prosthetic feet since it can determine their safety and lifetime. The elastomeric material should have high friction for safety, and at the same time it should have low wear for a longer lifetime. This research is aimed to study the optimum formulation of talc-powder-reinforced silicone elastomer to obtain high friction during sliding contact. The Taguchi orthogonal array L9 formula is used to achieve the aforementioned goal. The experiments use multiple parameters, namely, the type of silicone, the type of surface texture, the amount of catalyst, and the amount of talc powder. The results show that the combination of RTV 683, a smooth texture, 4% of catalyst, and 60% of talc powder is the most optimum composition to obtain the highest frictional force. It has a higher friction force in comparison with the imported products, and, at the same time, it has comparable wear with the imported products. The hardness of the optimized materials is comparable with the imported products. However, the tensile and tear strengths of the optimized materials need to be improved.

This paper demonstrates the analytical and numerical investigations for the obtainment of the predefined critical parameters of double-layer tape spring (DLTS) hinges. The DLTS hinge is utilized for the coupling between the solar panels to assist the accommodation and formulation of the assumed origami-based pattern of the solar arrays. They are examined for the assurance of safety, durability, non-permanent deformation, and stability from the stowed to the deployment configuration. Von Misses stress (σv) and steady-state moment simulations are investigated by varying the critical hinge design parameters of curvature radius (R), subtended angle (θ) and layer thickness (t). Two optimization models, Taguchi and response surface methodology/RSM, are utilized by employing the computational findings to obtain and validate the modified optimal geometric parameters within this analytical experiment. For the Taguchi method, the optimization of σv and the steady-state moment is accomplished with a t of 1.75–2.25 mm, R of 1.5–2.0 mm, and θ of 1–1.2°. Furthermore, the RSM model shows that the t, R, and θ parameters are determined to be 2.90 mm, 2 mm, and 1.35°, respectively. For optimization of the hinge design, both models should be considered for improved verification and accuracy of the results.

Djulis (Taiwanese quinoa) has gained popularity among researchers due to its biological characteristics and rich nutritional value. Incorporating Djulis is expected to enhance the sausage’s texture, flavor, and storage stability due to the presence of antioxidants and nutritional components. However, limited studies focus on product development based on this emerging health-promoting ingredient in the food industry. This study aims to develop Chinese-style sausage enriched with Djulis using the Taguchi L9(34) orthogonal matrix methodology and evaluate the influence of four factors, including un-hulled to hulled Djulis ratios of 0, 50, 100% (A), backfat-to-lean meat ratios of 0/100, 30/70, 50/50% (B), cooking temperature of 55, 75, 95 °C (C), and nitrite content of 0.03, 0.05, 0.07 g/kg (D) on products’ sensory and physicochemical properties. The optimal Taguchi formulation was then verified and compared with conventionally formulated sausage (original sausage) in terms of hardness, springiness, gumminess, CIE color values, and peroxide value (POV). The optimal formulation was A3B2C2D3, which consisted of 16.8% hulled Djulis, 30/70 backfat-to-lean meat ratio, 75 °C cooking temperature, and 0.03 g/kg nitrite content. The most influential independent parameters were identified as B > A > C > D, placing Djulis incorporation as the first runner-up, just after the backfat-to-lean meat ratio. Optimized condition verification identified the Signal-to-Noise ratio (S/N) of 16.63. Comparing the optimized Djulis-enriched sample and the original sausage indicated similar CIE L*, a*, b*, hardness, and springiness but different gumminess according to Texture Profile Analysis (TPA). The Djulis-enriched sausage at the optimized formulation had a significantly lower POV compared to the control sample (4.65 vs. 9.64 meq/kg), which was found to be correlated with Djulis antioxidant effects with 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) free radical antioxidant activity of 62.37%. This suggests that Djulis effectively mitigates sausage organoleptic deterioration. Djulis sausage, with natural antioxidants and reduced fat content, could cater to consumer preferences and enhance the market for the food industry and indigenous farmers.

This study prepares composite panels with three Polylactic acid (PLA)-based materials via the multi-material fused filament fabrication method. The influences of four processing parameters on the mechanical properties of 3D-printed samples are investigated employing the Taguchi method. These parameters include the relative volume ratio, material printing order, filling pattern, and filling density. A “larger is better” signal-to-noise analysis is performed to identify the optimal combination of printing parameters that yield maximum bending strength and bending modulus of elasticity. The results reveal that the optimal combination of printing parameters that maximizes the bending strength involves a volume ratio of 1:1:2, a material sequence of PLA/foam-agent-modified eco-friendly PLA (ePLA-LW)/glass fiber-reinforced eco-friendly PLA (ePLA-GF), a Gyroid filling pattern, and a filling density of 80%, and the optimal combination of printing parameters for maximum bending modulus involves a volume ratio of 1:2:1 with a material sequence of PLA/ePLA-LW/ePLA-GF, a Grid filling pattern, and 80% filling density. The Taguchi prediction method is utilized to determine an optimal combination of processing parameters for achieving optimal flexural performances, and predicted outcomes are validated through related experiments. The experimental values of strength and modulus are 43.91 MPa and 1.23 GPa, respectively, both very close to the predicted values of 46.87 MPa and 1.2 GPa for strength and modulus. The Taguchi experiments indicate that the material sequence is the most crucial factor influencing the flexural strength of the composite panels. The experiment result demonstrates that the flexural strength and modulus of the first material sequence are 67.72 MPa and 1.53 GPa, while the flexural strength and modulus of the third material sequence are reduced to 27.09 MPa and 0.72 GPa, respectively, only 42% and 47% of the first material sequence. The above findings provide an important reference for improving the performance of multi-material 3D-printed products.

The positive effect of decarbonizing the transport sector by using bio-based fuels is high. Currently, biodiesel and ethanol are the two biofuels that are blended with fossil fuels. Another technology, namely, hydroprocessing, is also gaining momentum for producing biofuels. Hydrotreated vegetable oil (HVO) produced using this process is a potential drop-in fuel due to its improved physiochemical properties. This study aimed to reduce the fossil diesel content by blending 20% and 30% HVO and 5%, 10% and 15% waste cooking oil biodiesel on a volume basis. The blends were used to conduct a thorough performance examination of a single-cylinder compression ignition engine. The thermal efficiency of the engine was enhanced by the addition of biodiesel to the blend. The efficiency increased as the proportion of biodiesel in the mix increased, although it was still less efficient than diesel. The maximum improvement in thermal efficiency of 4.35% was observed with 20% blending of HVO and 15% blending of biodiesel compared with 20% blending of HVO and diesel. However, the HC (decrease of 30%), CO (decrease of 23.5%) and smoke (decrease of 21.1%) emissions were observed to be the lowest with 30% blending of HVO and 15% blending of biodiesel. A fuzzy-logic-based Taguchi method and Grey’s method were then applied to find the best blend of HVO, biodiesel and diesel. The combination of the two methods made it easier to carry out multi-objective optimization. The brake thermal efficiency (BTE), smoke and NO emissions were selected as the output parameters to optimize the HVO and biodiesel blend. The optimization study showed that 30% blending of HVO and 15% blending of biodiesel was the best blend, which was authenticated using the confirmation experiment.

Plastic pervasiveness in daily life has increased in tandem with population growth. Ethylene–vinyl acetate (EVA) is emerging as a popular compound for manufacturing plastic, which is obtained from ethylene and vinyl acetate synthesis. EVA is produced using autoclave reactors, which often encounter bearing damage under specific operating conditions. This research aims to optimize the parameters in autoclave reactors to enhance bearing life. The study focuses on two crucial factors: the number of impellers and the temperature, with bearing life as the response variable. Simulations using finite-element analysis were conducted to obtain the fatigue life of bearings and validated using real-time company data stating the damage of bearings within 80 days. The optimization process employed the Taguchi method (TM) and the response surface methodology (RSM). A comparison of these techniques revealed that temperature had the most significant influence on the response. Interestingly, both methods yielded the same optimal parameters: seven impellers and a temperature of 150 °C. The simulation results using these optimized parameters demonstrated a noteworthy 3.095% increase in bearing life compared to the initial design. The RSM outperformed the Taguchi method in accurately predicting response values with minimum prediction error under optimal conditions.

Mechanical engineering plays an important role in the design and manufacture of medical devices, implants, prostheses, and other medical equipment, where the machining of bio-compatible materials have a special place. There are a lot of different conventional and non-conventional types of machining of biocompatible materials. One of the most frequently used methods is milling. The first part of this research explores the machining parameters optimization minimizing surface roughness in milling titanium alloy Ti-6Al-4V. A full factorial design involving four factors (cutting speed, feed rate, depth of cut, and the cooling/lubricating method), each having three levels, implies the 81 experimental runs. Using the Taguchi method, the number of experimental runs was reduced from 81 to 27 through an orthogonal design. According to the analysis of variance (ANOVA), the most significant parameter for surface roughness is feed rate. The second part explores the possibilities of using different ML techniques to create a predictive model for average surface roughness using the previously created small datasets. The paper presents a comparative analysis of several commonly used techniques for handling small datasets and regression problems. The best results indicate that the widely used machine learning algorithm Random Forest excels in handling regression problems and small datasets.

Magnesium, aluminum, and iron electrode were common anodes in electrocoagulation (EC), but there were few studies comparing the influence of operating parameters on the reaction and the effects of these three different anodes in removing total phosphorus (TP) in cattle farm wastewater (CFW). This study used these three different electrodes as the anode of the electrocoagulation method. The operating parameters such as electrode distance (ED), initial pH, and voltage were examined by the Taguchi method and single-factor method. The result of Taguchi analysis shows that voltage has a significant impact on TP removal of magnesium (Mg) and aluminum anode (Al), and ED has a significant impact on TP removal of magnesium and iron anode (Fe). Among the three operating parameters, the first in the order of the impact on the reaction was ED because ED has the greatest influence on the dissolution of the anode. The result of single-factor analysis shows that the optimum conditions of Mg were ED = 3 cm, voltage = 5 V, pH = 7, Al were ED = 3 cm, voltage = 5 V, pH = 5, and Fe were ED = 3 cm, voltage = 5 V, and pH = 7. For Mg and Fe, the voltage and reaction results follow the second-order reaction kinetics, and Al is the first-order reaction kinetics. Through comparison of the three anodes, it was found that Al worked best in actual CFW. It was noted that the conditions obtained by the single-factor method were more economical in the treatment of actual CFW. This work could provide reference for determining the extent of the influence of operating parameters when other contaminants are removed, and provide reference for comparing the other anodes in EC.

The rice husk (RH) combustion pretreatment method plays a crucial role in the extraction of nanoscale SiO2 from RH as a silicon source. This study examined the effects of diverse pretreatment methods and combustion temperatures on the particle size distribution of nanoscale high-purity amorphous SiO2 extracted from rice husk ash (RHA) post RH combustion. The experiment was structured using the Taguchi method, employing an L9 (21 × 33) orthogonal mixing table. The median diameter (D50) served as the output response parameter, with the drying method (A), combustion temperature (B), torrefaction temperature (C), and pretreatment method (D) as the input parameters. The results showed the torrefaction temperature (C) as being the predominant factor affecting the D50, which decreased with an increasing torrefaction temperature (C). The optimal parameter combination was identified as A2B2C3D2. The verification test revealed that roasting could improve the abrasiveness of Rh-based silica and reduce the average particle size. Torrefaction at medium temperatures might narrow the size distribution range of RHA-SiO2. We discovered that the purity of silica increased with an increasing roasting temperature by evaluating the concentration of silica in the sample. The production of RHA with silica concentrations up to 92.3% was investigated. X-ray diffraction analysis affirmed that SiO2’s crystal structure remained unaltered across different treatment methods, consistently presenting as amorphous. These results provide a reference for extracting high-value products through RH combustion.

Robust and well-designed rotor-bearing systems ensure safe operation and a high level of reliability under severe operating conditions. A deviation in the shaft axis with respect to the bearing longitudinal axis represents one of the most unavoidable problems in bearing systems. This deviation results from installation errors, manufacturing errors, shaft deformation under heavy loads, bearing wear, and many other causes. Each of these deviation sources has its negative consequences on the designed characteristics of the system. This work deals with the geometrical design of a journal bearing using three forms of profiles (linear (n=1), quadratic n=2 and cubic (n=3) profiles) in order to enhance bearing performance despite the presence of the inevitable shaft deviation. In addition, a wide range of bearing profile parameters are considered in the analysis to optimize the bearing profile based on the use of the Taguchi method. A general form of shaft deviation is considered to account for both horizontal and vertical deviations. A numerical solution is obtained using the finite difference method. The results show that all three suggested forms of bearing profiles elevate the film thickness significantly and also reduce the friction coefficient, but with different effects on the maximum pressure values. The Taguchi method illustrates that the optimal geometrical design parameters are the quadratic profile and the modification of one-fifth of the bearing width from both sides at a height of just less than half the radial clearance (0.4 C) at the bearing edges. These values give the best combination of the considered main bearing characteristics: the minimum film thickness, coefficient of friction, and maximum pressure. The results show that the minimum film thickness is increased by 184%, the maximum pressure is reduced by 15.1% and the friction coefficient is decreased by 6.4% due to the use of the suggested design. The outcome of this work represents an important enhancement step for the rotor bearing performance to work safely with high reliability under severe shaft deviation levels. This can be implied at the design stage of the bearing, which requires prior knowledge about the operating conditions in order to have better estimation for the levels of shaft deviation.

In this work, a new lignocellulosic adsorbent was obtained and tested for crystal violet dye removal from water. The material was obtained from hart’s-tongue fern (Asplenium scolopendrium) leaves after minimal processing, without chemical or thermal treatment. The surface of the material was characterized using a variety of techniques, including FTIR, SEM, and color analysis. The effect of various factors on the adsorption capacity was then investigated and discussed. The kinetic and equilibrium studies showed that the general-order kinetic model and the Sips isotherm are the most suitable to describe the adsorption process. The equilibrium time was reached after 20 min and the maximum calculated value of the adsorption capacity was 224.2 (mg g−1). The determined values for the thermodynamic parameters indicated physical adsorption as the main mechanism involved in the process. The Taguchi method was used to optimize the adsorption conditions and identify the most influential controllable factor, which was pH. ANOVA (general linear model) was used to calculate the percentage contribution of each controllable factor to dye removal efficiency. Analysis of all the results shows that hart’s-tongue fern (Asplenium scolopendrium) leaves are a very inexpensive, readily available, and effective adsorbent for removing crystal violet dye from aqueous solutions.

Current methods for sleep stage detection rely on sensors to collect physiological data. These methods are inaccurate and take up considerable medical resources. Thus, in this study, we propose a Taguchi-based multiscale convolutional compensatory fuzzy neural network (T-MCCFNN) model to automatically detect and classify sleep stages. In the proposed T-MCCFNN model, multiscale convolution kernels extract features of the input electroencephalogram signal and a compensatory fuzzy neural network is used in place of a traditional fully connected network as a classifier to improve the convergence rate during learning and to reduce the number of model parameters required. Due to the complexity of general deep learning networks, trial and error methods are often used to determine their parameters. However, this method is very time-consuming. Therefore, this study uses the Taguchi method instead, where the optimal parameter combination is identified over a minimal number of experiments. We use the Sleep-EDF database to evaluate the proposed model. The results indicate that the proposed T-MCCFNN sleep stage classification accuracy is 85.3%, which is superior to methods proposed by other scholars.

Mycelia products enhance edible mushrooms in alignment with future sustainability trends. To meet forthcoming market demands, the morphology of mycelial pellets was optimized for direct consumption. Among ten commercial edible mushrooms in Taiwan, Pleurotus sp. was selected for its rapid growth and was identified via an internal transcribed spacer sequence. A combination of Plackett-Burman design and Taguchi’s L9(34) orthogonal table revealed the optimal formula as potato dextrose broth (2.4%), olive oil (2%), calcium carbonate (0.5%), yeast extract (0.75%), and soy flour (0.5%). This led to a biomass increase to 19.9 ± 1.1 g/L, resulting in a 2.17-fold yield increase. To refine morphology, image processing by ImageJ quantified spherical characteristics. The addition of 0.2 to 1.0% Tween 80 enhanced pellet compaction by over 50%. Dilution of the medium improved uniformity (0.85) and conversion rate (42%), yielding mycelial pellets with 2.10 ± 0.52 mm diameters and a yield of 15.1 ± 0.6 g/L. These findings provide an alternative evaluation and application of edible mycelial pellets as future food.

The tightening torque values considered in the assembly of vehicle subparts are of great importance in terms of connection safety. The torque value to be selected is different for each bolted joint type with respect to mechanical features. While the tightening torque value is an important indicator, the bolt preloading value is always a more reliable parameter in terms of whether a secure tightening can be achieved or not. For this reason, when it is desired to create reliable joints, the preloading value that the tightening torque input will create on the connection package should be calculated well. This study presents an integrated approach using Taguchi method (TM) and neural network (NN) to predict the self-loosening mechanism of bolted joints in automotive chassis engine suspension connections. External loading acting on the joints of the engine suspension was collected from bench tests. NN was applied to establish the relationship between controlled factors and loosening rate. The results showed that the proposed approach can be used to predict mechanism of self-loosening and behavior of bolted joints without additional tests, and it is possible to make predictions with very low error rates using artificial intelligence techniques.

The combustion of wood pellets in a fixed bed combustor of a 20 kW capacity domestic pellet boiler was tested according to several factors including Power, Excess Air (EA), Primary/Secondary air Split Ratio (SR) and Grate Area (GA). The Taguchi method was applied to program the experimental design. Several parameters were measured, including gas emissions (CO), fuel bed temperature (measured at 4 different heights), and efficiency. The experimental results show that the lower CO emission and the higher efficiency were obtained at medium thermal loads and the highest temperature on the fuel bed was obtained at about ¼ of its height (15 mm). The results obtained from the analysis of variance (ANOVA) show that the SR and the Power are the most important factors contributing to the CO reduction and also increase the fuel bed temperature.

The present article describes the tribological properties of Mg-based hybrid composites reinforced with graphite nanofiber (GNF) and alumina short fiber (Al2O3sf) that were investigated. The Mg/GNF/Al2O3sf hybrid composites with varying volume fraction of fiber (10 vol.%, 15 vol.%, 20 vol.%) were developed. SEM observations indicate that the GNF cluster distributions within the array of the Al2O3sf network are found to be relatively good. The Taguchi design of the experiment has been applied to conduct the wear test, and the statistical analysis of variance (ANOVA) has been used to evaluate the influence of wear test parameters on the wear loss and coefficient of friction (COF) of the composites. The influence of wear test parameters such as volume fraction of fiber (VF), applied load (AL), sliding distance (SD), and sliding speed (SP) on the wear loss and COF of composites was analyzed under dry sliding conditions. The results of ANOVA indicate that the sliding distance was found to be the prominent factor affecting wear loss, and the applied load influenced the COF most significantly. Furthermore, the composites with 20 vol.% of fiber had lower wear loss than those with 10 vol.% and 15 vol.% of fiber. The COF of composites with 15 vol.% of fiber was found to be slightly lower compared to the 10 vol.% and 20 vol.% of fiber cases. The results imply that the hybridization of GNFs and Al2O3sf, as well as the formation of Mg17Al12 and Al2MgC2 precipitates enhanced the tribological properties of the Mg hybrid composites.

Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data in order to reduce the computational complexity of model development and improve generalization performance. Ensemble feature-ranking methods combine the results of several feature-selection techniques to identify a subset of the most relevant features for a given task. In many cases, they produce a more comprehensive ranking of features than the individual methods used alone. This paper presents a novel approach to ensemble feature ranking, which uses a weighted average of the individual ranking scores calculated using these individual methods. The optimal weights are determined using a Taguchi-type design of experiments. The proposed methodology significantly improves classification performance on the CSE-CIC-IDS2018 dataset, particularly for attack types where traditional average-based feature-ranking score combinations result in low classification metrics.

This study aims to optimize the flow channel design for a proton exchange membrane electrolyzer cell (PEMEC) to minimize the pressure drop across the cell. The pattern of parallel flow channels is considered with a dual-porous layer structure sandwiched between the flow channel plate and the catalyst layer. Four geometric factors are considered in the optimization analysis, including the width of the flow channel, the depth of the flow channel, the particle diameter of the large-pore porous layer, and the particle diameter of the small-pore porous layer. Computational fluid dynamics (CFD) is used to simulate the flow field, and based on the results of the CFD simulation, the Taguchi method is employed to analyze the optimal flow channel design. The importance of the factors is further analyzed by the analysis of variance (ANOVA) method. Three inlet velocities are assigned in the Taguchi analysis, which are 0.01, 0.1332, and 0.532 m/s, and then an orthogonal array is constructed and analyzed for each inlet flow condition. It is found that the optimal combination of the factors is the depth of the flow channel 1 mm, the width of the flow channel 3 mm, the particle diameter of the large-pore porous layer 0.212 mm, and the particle diameter of the small-pore porous layer 0.002 mm. The pressure drop across the PEMEC is minimized at the condition with the optimal combination of the factors. The ANOVA analysis shows that the depth of the flow channel exhibits the most significant impact on the pressure drop, while the other factors play minor roles only.

The strip filling method in underground reservoir needs high strength to achieve the requirements of water storage. In order to address the challenges associated with costly and weak filling materials, this study aimed to develop an economically efficient and high-strength gangue-based geopolymer backfill material (GBGBM). To achieve this, the Taguchi method was employed to design a series of 25 experiments, each consisting of four factors and five levels. This study focused on investigating the effects of different gangue gradation levels, sand ratios, water binder ratios (w/b), and aggregate binder ratios (a/b) on the working characteristics and unconfined compressive strength (UCS) of the GBGBM. The optimal combination of the GBGBM was determined by employing a signal-to-noise ratio (S/N)-based extreme difference and variance analysis. The results revealed that the w/b ratio exerted the most substantial influence on both the slump and UCS. Specifically, when employing a gradation of 50%, a sand ratio of 55%, an a/b ratio of 2.5, and a w/b ratio of 0.64, the slump measured 251.2 mm, the UCS at 3d reached 5.27 MPa, and the UCS at 28d amounted to 17.65 MPa. These findings indicated a remarkable improvement in early UCS by 131.14% and the late UCS by 49.45% compared to gangue-based cement backfill material (GBCBM). Additionally, this study examined the hydration products and microstructures of both GBGBM and GBCBM using X-ray diffraction (XRD), scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) analyses. Significantly, the GBGBM exhibited notable advantages over the GBCBM, including a 78.16% reduction in CO2 emissions, a 73.45% decrease in energy consumption, and a 24.82% reduction in cost. These findings underscore the potential of GBGBM as a sustainable and cost-effective alternative to GBCBM.

This paper introduces a novel approach to deal with the multi-objective optimization of a two-stage bevel helical gearbox by applying the Taguchi method and Grey Relation Analysis (GRA). The goal of the study is to find optimal main design factors that minimize the gearbox volume and maximize the gearbox efficiency. To accomplish this, five main design parameters were selected: the coefficients of wheel face width (CWFW) of the bevel and the helical gear sets, the allowable contact stresses (ACS) of the first and the second stages, and the gear ratio of the first stage. Furthermore, two single targets were investigated: minimum gearbox volumes, and maximum gearbox efficiency. Also, the multi-objective optimization problem is solved through two steps: Step 1 for closing the gap between variable levels and Step 2 for determining the optimal main design factors. The study’s findings were used to introduce the optimum values of five major design parameters for designing a two-stage helical gearbox.

This paper discusses the mixing of polylactide (PLA) and glass fiber which use injection molding to produce a functional composite material with glass fiber properties. The injection molding process explores the influence of glass fiber ratio, melt temperature, injection speed, packing pressure, packing time and cooling time on the mechanical properties of composite. Using the orthogonal table planning experiment of the Taguchi method, the optimal parameter level combination of a single quality process is obtained through main effect analysis (MEA) and Analysis of variance (ANOVA). Then, the optimal parameter level combination of multiple qualities is obtained through principal component analysis (PCA) and data envelopment analysis (DEA), respectively. It is observed that if all the quality characteristics of tensile strength, hardness, impact strength and bending strength are considered at the same time, the optimal process conditions are glass fiber addition 20 wt %, melt temperature 185 °C, injection speed 80 mm/s, holding pressure 60 MPa, holding time 1 s and cooling time 15 s, and the corresponding mechanical properties are tensile strength 95.04 MPa, hardness 86.52 Shore D, impact strength 4.4408 J/cm2, bending strength 119.89 MPa. This study effectively enhances multiple qualities of PLA/GF composite.

Heuristic optimization algorithms have been proved to be powerful in solving nonlinear and complex optimization problems; therefore, many effective optimization algorithms have been applied to solve optimization problems in real-world scenarios. This paper presents a modification of the recently proposed Gaining–Sharing Knowledge (GSK)-based algorithm and applies it to optimize resource scheduling in the Internet of Vehicles (IoV). The GSK algorithm simulates different phases of human life in gaining and sharing knowledge, which is mainly divided into the senior phase and the junior phase. The individual is initially in the junior phase in all dimensions and gradually moves into the senior phase as the individual interacts with the surrounding environment. The main idea used to improve the GSK algorithm is to divide the initial population into different groups, each searching independently and communicating according to two main strategies. Opposite-based learning is introduced to correct the direction of convergence and improve the speed of convergence. This paper proposes an improved algorithm, named parallel opposition-based Gaining–Sharing Knowledge-based algorithm (POGSK). The improved algorithm is tested with the original algorithm and several classical algorithms under the CEC2017 test suite. The results show that the improved algorithm significantly improves the performance of the original algorithm. When POGSK was applied to optimize resource scheduling in IoV, the results also showed that POGSK is more competitive.

The structure of fishnet knots has been simplified in previous studies to facilitate the construction of numerical equations of the fishnet structure. This leads to errors in the dynamic analysis of the trawl mesh structure with water flow. In this study, the finite element method was used to analyze the interaction of the trawl mesh structure with the solid object in a dynamic explicit environment. At the same time, design variables were optimized through impact assessment and the displacement of grid cells. The results show that the polyamide (PA) material, a 0.4 mm cross-section, and a 25 mm mesh size are the optimal choices. When the displacement speed of the solid body increased, the displacement and collision values of the mesh structure tended to increase gradually along the quadratic curve. Confirmation tests performed on the tensile tester machine showed a good load-carrying capacity of up to 1280 MPa for trawl mesh structures using the PA material. The characteristic curve for the stress of the trawl mesh structure is shown through the higher-order curve.